System and method for low latency edge computing

ABSTRACT

Aspects of the subject disclosure may include, for example, a method in which a processing system receives data at an edge node of a network that also includes regional nodes and central nodes. The processing system also determines a latency criterion associated with an application for processing the data; the application corresponds to an application programming interface. The method also includes processing the data in accordance with the application, monitoring a latency associated with the processing, and determining whether the latency meets the latency criterion. The processing system dynamically assigns data processing resources so that the latency meets the latency criterion; the resources include computation, network and storage resources of the edge node, a central node, and a regional node in communication with the edge node and the central node. Other embodiments are disclosed.

FIELD OF THE DISCLOSURE

The subject disclosure relates to a system and method for low latencycomputing, and more particularly to a distributed system including edgecomputing, regional computing and central computing that enables lowlatency computing.

BACKGROUND

Next-generation applications, powered by advances in machine learning,autonomous vehicles, and virtual and augmented reality, will in manycases require near-real-time responses from computing systems. Computingpower may be deployed at network edges, i.e. in data centers ofrelatively small capacity, scattered in and around a populated area, ina distributed computing architecture (referred to herein as an edgenetwork or fog network).

BRIEF DESCRIPTION OF THE DRAWINGS

Reference will now be made to the accompanying drawings, which are notnecessarily drawn to scale, and wherein:

FIG. 1 is a block diagram illustrating an exemplary, non-limitingembodiment of a communications network in accordance with variousaspects described herein.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system functioning within the communication network ofFIG. 1 and including fog nodes and regional nodes communicating with acloud network, in accordance with various aspects described herein.

FIG. 2B schematically illustrates components of a regional network node,in accordance with embodiments of the disclosure.

FIG. 2C schematically illustrates a computing architecture and latencytimes associated with various network nodes, in accordance withembodiments of the disclosure.

FIG. 2D schematically illustrates a fog node of a low latency network inaccordance with embodiments of the disclosure.

FIG. 2E illustrates an example of low latency edge computing using anetwork architecture according to embodiments of the disclosure.

FIG. 2F illustrates data processing for a first application requiringrelatively low-latency and a second application with higher latency, inaccordance with an embodiment of the disclosure.

FIG. 2G schematically illustrates partitioning of an applicationprogramming interface (API) into a set of base APIs accessing a set ofdata objects.

FIG. 2H schematically illustrates an architecture for dynamicoptimization of latency in a central network node, in accordance with anembodiment of the disclosure.

FIG. 2I is a flowchart depicting an illustrative embodiment of a methodfor optimizing latency in a central network node, in accordance withvarious aspects described herein.

FIG. 2J is a flowchart depicting an illustrative embodiment of a methodfor optimizing latency with respect to data placement and/or APIplacement in a network, in accordance with various aspects describedherein.

FIG. 3 is a block diagram illustrating an example, non-limitingembodiment of a virtualized communication network in accordance withvarious aspects described herein.

FIG. 4 is a block diagram of an example, non-limiting embodiment of acomputing environment in accordance with various aspects describedherein.

FIG. 5 is a block diagram of an example, non-limiting embodiment of amobile network platform in accordance with various aspects describedherein.

FIG. 6 is a block diagram of an example, non-limiting embodiment of acommunication device in accordance with various aspects describedherein.

DETAILED DESCRIPTION

The subject disclosure describes, among other things, illustrativeembodiments of a distributed computing architecture that enables lowlatency computing. Other embodiments are described in the subjectdisclosure.

One or more aspects of the subject disclosure include a methodcomprising receiving, by a processing system including a processor, dataat an edge node of a plurality of edge nodes of a network; the networkincludes a plurality of regional nodes and a plurality of central nodes.(As is understood in the art, the network typically includes othernetwork elements and devices and can communicate with user equipment.)The method also comprises determining a latency criterion associatedwith an application for processing the data; the application utilizes anapplication programming interface (API) included in a databaseaccessible to a central node of the plurality of central nodes. (Invarious embodiments, the API can be accessible to edge nodes andregional nodes, and portions of the API can be accessible to users ofthe application, who may, for example, be subscribers to the network.)The method also comprises monitoring a latency associated withprocessing the data by the application, and determining whether thelatency satisfies the latency criterion. The method further comprisesassigning resources to assist the application in reducing the latencyassociated with the processing of the data, in response to the latencysatisfying the latency criterion; the resources comprise computationresources, storage resources, network resources or a combination ofthose resources obtained from the central node, the edge node, aregional node of the plurality of regional nodes in communication withthe edge node and the central node, or a combination of those nodes.

One or more aspects of the subject disclosure include a devicecomprising a processing system and a memory storing executableinstructions that, when executed by the processing system, facilitateperformance of operations. The operations comprise receiving data at anedge node of a plurality of edge nodes of a network; the networkincludes a plurality of regional nodes and a plurality of central nodes.The operations also comprise determining a latency criterion associatedwith an application for processing the data; the application utilizes anapplication programming interface (API) included in a databaseaccessible to a central node of the plurality of central nodes (and canalso be accessible to edge and regional nodes and to users, as notedabove). The operations also comprise measuring and/or accessing a keyperformance indicator (KPI) of the network, monitoring a latencyassociated with processing the data by the application, and determiningwhether the latency satisfies the latency criterion. The operationsfurther comprise assigning resources to assist the application inreducing the latency associated with the processing of the data, inresponse to the latency satisfying the latency criterion; the resourcescomprise computation resources, network resources, storage resources ora combination of those resources obtained from the central node, theedge node, a regional node of the plurality of regional nodes incommunication with the edge node and the central node, or a combinationof those nodes.

One or more aspects of the subject disclosure include a machine-readablemedium comprising executable instructions that, when executed by aprocessing system including a processor, facilitate performance ofoperations. The operations comprise receiving data at an edge node of aplurality of edge nodes of a network; the network includes a pluralityof regional nodes and a plurality of central nodes. The operations alsocomprise determining a latency criterion associated with an applicationfor processing the data; the application utilizes an applicationprogramming interface (API) included in a database accessible to acentral node of the plurality of central nodes, the API comprises aplurality of base APIs, and the database includes a list of data objectsto be accessed by the respective base APIs. The operations also comprisemonitoring a latency associated with processing the data by theapplication, and determining whether the latency satisfies the latencycriterion. The operations further comprise assigning resources to assistthe application in reducing the latency associated with the processingof the data, in response to the latency satisfying the latencycriterion; the resources comprise computation resources, storageresources, network resources or a combination of those resourcesobtained from the central node, the edge node, a regional node of theplurality of regional nodes in communication with the edge node and thecentral node, or a combination of those nodes.

Referring now to FIG. 1, a block diagram is shown illustrating anexample, non-limiting embodiment of a communications network 100 inaccordance with various aspects described herein. For example, network100 can facilitate in whole or in part communications between edgenodes, regional nodes, and central nodes to enable low-latencycomputing. In particular, a communications network 125 is presented forproviding broadband access 110 to a plurality of data terminals 114 viaaccess terminal 112, wireless access 120 to a plurality of mobiledevices 124 and vehicle 126 via base station or access point 122, voiceaccess 130 to a plurality of telephony devices 134, via switching device132 and/or media access 140 to a plurality of audio/video displaydevices 144 via media terminal 142. In addition, communication network125 is coupled to one or more content sources 175 of audio, video,graphics, text and/or other media. While broadband access 110, wirelessaccess 120, voice access 130 and media access 140 are shown separately,one or more of these forms of access can be combined to provide multipleaccess services to a single client device (e.g., mobile devices 124 canreceive media content via media terminal 142, data terminal 114 can beprovided voice access via switching device 132, and so on).

The communications network 125 includes a plurality of network elements(NE) 150, 152, 154, 156, etc. for facilitating the broadband access 110,wireless access 120, voice access 130, media access 140 and/or thedistribution of content from content sources 175. The communicationsnetwork 125 can include a circuit switched or packet switched network, avoice over Internet protocol (VoIP) network, Internet protocol (IP)network, a cable network, a passive or active optical network, a 4G, 5G,or higher generation wireless access network, WIMAX network,UltraWideband network, personal area network or other wireless accessnetwork, a broadcast satellite network and/or other communicationsnetwork.

In various embodiments, the access terminal 112 can include a digitalsubscriber line access multiplexer (DSLAM), cable modem terminationsystem (CMTS), optical line terminal (OLT) and/or other access terminal.The data terminals 114 can include personal computers, laptop computers,netbook computers, tablets or other computing devices along with digitalsubscriber line (DSL) modems, data over coax service interfacespecification (DOCSIS) modems or other cable modems, a wireless modemsuch as a 4G, 5G, or higher generation modem, an optical modem and/orother access devices.

In various embodiments, the base station or access point 122 can includea 4G, 5G, or higher generation base station, an access point thatoperates via an 802.11 standard such as 802.11n, 802.11ac or otherwireless access terminal. The mobile devices 124 can include mobilephones, e-readers, tablets, phablets, wireless modems, and/or othermobile computing devices.

In various embodiments, the switching device 132 can include a privatebranch exchange or central office switch, a media services gateway, VoIPgateway or other gateway device and/or other switching device. Thetelephony devices 134 can include traditional telephones (with orwithout a terminal adapter), VoIP telephones and/or other telephonydevices.

In various embodiments, the media terminal 142 can include a cablehead-end or other TV head-end, a satellite receiver, gateway or othermedia terminal 142. The display devices 144 can include televisions withor without a set top box, personal computers and/or other displaydevices.

In various embodiments, the content sources 175 include broadcasttelevision and radio sources, video on demand platforms and streamingvideo and audio services platforms, one or more content data networks,data servers, web servers and other content servers, and/or othersources of media.

In various embodiments, the communications network 125 can includewired, optical and/or wireless links and the network elements 150, 152,154, 156, etc. can include service switching points, signal transferpoints, service control points, network gateways, media distributionhubs, servers, firewalls, routers, edge devices, switches and othernetwork nodes for routing and controlling communications traffic overwired, optical and wireless links as part of the Internet and otherpublic networks as well as one or more private networks, for managingsubscriber access, for billing and network management and for supportingother network functions.

FIG. 2A is a block diagram illustrating an example, non-limitingembodiment of a system 201 functioning within the communication networkof FIG. 1 in accordance with various aspects described herein. As shownin FIG. 2A, a low-latency network architecture includes network edgenodes 211-1, 211-2, 211-3 (also referred to herein as fog nodes)receiving data 215 from various devices 210 (for example, sensorsincluded in the “Internet of things” or IoT); such devices are generallyreferred to herein as edge devices. Regional nodes 212-1, 212-2 receiveand process data from the fog nodes, and communicate with a central node213 of the cloud.

The fog node(s) may process the data ingested from the edge devicesaccording to a low-latency application, to provide a real-time response(that is, a response within approximately 10 msec) depending on theapplication requirements. The fog node is advantageously located closeto the source of the data.

In an embodiment, the fog nodes may utilize streaming frameworks (e.g.,Apache Storm, Apache Flink, Spark). In a further embodiment, the fognodes stream data (either processed or unprocessed) toward the regionalnodes using a stream processing platform (e.g. Kafka).

In another embodiment, the fog nodes are configured to forward the datato third-party applications; for example, via a third-party applicationprogramming interface (API) manager 217. The third-party API managerprovides a gatekeeping function for transferring data to the thirdparty. In this embodiment, a third party can have access to the edgedevice data 215 with very low latency relative to access by routingthrough a core network.

The regional nodes 212-1, 212-2 include processors for analyzing thedata received from the fog nodes. In an embodiment, the regionalprocessors can also incorporate complementary data from the cloud whenperforming this analysis. In another embodiment, the regional nodesreceive data as it is ingested by the fog nodes; data forwarded to thecloud by the regional nodes may thus be either processed or unprocessed.

FIG. 2B is a schematic illustration 202 of components of a regional node220: controller 221, processor 222, and measurement agent 223. Regionalcontroller 221 manages the node in coordination with the fog nodes andthe cloud. Processor 222 analyzes data from the edge devices, with ahigher latency compared to the fog nodes. The regional measurement agent223 measures latencies between the fog nodes, regional node, and thecloud. In an embodiment, the measurement agent measures other keyperformance indicators (KPIs) of the network (e.g., data flow size, dataflow rate, data loss rate) in addition to, or instead of, latency of theregional node. In an embodiment, the measurement agent 223 forms part ofa measurement network for measuring KPIs between the network edge andthe cloud.

Referring again to FIG. 2A, the cloud (represented as a central node213) includes a service node 213-1 and a monitoring server 213-2. Thecloud may perform additional processing on the data received from theregional nodes; this processing is generally not in real time. In anembodiment, data at the cloud is formatted as files and stored instorage device 219.

In an embodiment, service node 213-1 communicates with the edge devicesand maintains a list of available services. The list of availableservices may include descriptions of the services, latency requirements,and other quality of service (QoS) parameters associated with theservices. In an embodiment, the service node provides a servicerepository API that can be used by either edge devices or third-partyapplications. In a further embodiment, the API can be utilized byoptimization services including a data placement optimization engine214, a traffic routing optimization engine 215, and a API queryoptimization engine 216.

The edge and regional monitoring server 213-2 records data relating tothe health of the edge and regional nodes. In an embodiment, themonitoring server stores inter-node and intra-node latencies for the fognodes and the regional nodes.

The monitoring server can measure the latency for each API for eachnetwork user. In an embodiment, the monitoring server monitors latenciesand other KPIs between edge nodes, regional nodes, and the cloud. In anfurther embodiment, the monitoring server 213-2 controls measurementagents 223 in the regional nodes and measurement agents in the fognodes.

FIG. 2C is a schematic illustration 203 of a computing architecture andlatency times associated with various network nodes, in accordance withembodiments of the disclosure. As shown in FIG. 2C, streamingtechnologies may be used at the fog nodes and regional nodes. In thisembodiment, data is streamed from the edge data ingestion component231-1 of fog node 231 to the computing component 231-2 of the fog node,and from the regional data ingestion component 232-1 of regional node232 to the computing component 232-2 of regional node 232.

In this embodiment, the edge data ingestion component 231-1 collectsreal-time user session event data from network nodes (e.g., eNodeBs, CU,DU, MMEs) and from user equipment (UE) at the fog nodes. Data from UEsmay be transmitted to the fog nodes via a cellular base station 230. Avariety of functions may thus be enabled, for example: near real-timenetwork problem isolation and trouble-shooting, cell boundary detection,geo-tagging and closed-loop RAN management and optimization. Inparticular, streamed data 236-1 can support latency-sensitive businessapplications. As shown in FIG. 2C, data can be ingested and processed atthe fog node with a relatively short latency time t₁. The edge dataingestion component 231-1 may also stream data 237 to third party realtime applications.

Regional node 232 may also receive output streams 236-2 from the edgedata ingestion component of fog node 231. In an embodiment, the regionaldata ingestion component 232-1 pre-processes the streaming datareceived, and streams the processed data 236-3 as output. The regionaldata ingestion component 232-1 may also output data 238 as files whichcan be consumed by downstream applications that are insensitive to datalatency, and can be stored in file-based storage 235.

The service node 233-1 and optimization engine 233-2 of central node 233are enabled to meet service level agreements (SLAs) regarding latency orother KPIs. In this embodiment, the service node 233-1 includes an APIdatabase containing information regarding various supported APIs andservice level agreements (SLAs). In general, each application has alatency requirement and SLA associated therewith; the SLA specifiesquality of service (QoS) requirements. The optimization engine 233-2determines whether that SLA is being met, and enforces the SLA byoptimizing placement of base applications, placement of data, androuting of network traffic. In an embodiment, the optimization engine233-2 predicts whether the SLA for an application will be met, based onthe latency achieved for the application.

FIG. 2C also shows relative latencies 239 for data processing at the fognode, regional node and central node. In this embodiment, latency timet₁ can be on the order of milliseconds for processing at the fog node231 (sometimes referred to as near-real-time), while latency time t₂ canbe on the order of seconds for processing at the regional node 232, andlatency time t₃ can be on the order of minutes for processing at thecentral node 233.

FIG. 2D is a schematic illustration 204 of a fog node 241 of a lowlatency network in accordance with embodiments of the disclosure. Inthis embodiment, the fog node includes a thin core processing system 242has an SAE (service architecture evolution) node that performs functionsof a gateway, e.g., a service gateway (S-GW) and/or a packet datanetwork gateway (P-GW). The data collected at the edge can be tunneledvia the SAE to a third party. The thin core also provides security anddata anonymization engines for security and preserving privacy.

In an embodiment, the third-party API manager 217 provides a list ofAPIs that a third party can use to access edge data. Data may bedirectly forwarded to the third party in accordance with the accesspoint name (APN) of the data. In a further embodiment, the API managermay request processing of the ingested data before it is forwarded tothe third party.

The fog node may include local complementary storage 243. In anembodiment, storage 243 contains information required for applyingdecentralized machine-learning/artificial-intelligence (ML/AI)algorithms from neighboring nodes. Such information can be used indelay-sensitive applications, e.g. safety applications for autonomousvehicles.

In this embodiment, the fog node also includes a software-definednetwork (SDN) enabled router 244 and/or an SDN enabled switch. Router244 facilitates communication between the fog nodes and regional nodes,and can adaptively control traffic flow; in particular, router 244 mayroute information over different routes so that data transmissionlatency is minimized. More generally, SDN capability at the fog node canenable creation of a specific channel for sharing local awarenessbetween fog nodes. In an embodiment, a fog node using this channel canshare local measurements and any other information (e.g. resourceusages, number of devices observed, etc.) that other nodes may requirefor making a decision. For example, if a fog node is busy withcomputations, other nodes may temporarily avoid asking that node for anyadditional processing.

The edge computing unit 245 is the main local processing unit of node241 and performs various functions including, for example, dataanalysis, data encoding and decoding, and applying algorithms forefficient data processing.

In this embodiment, a local measurement agent 246 communicates withcomputing unit 245. The local measurement agent can measure latenciesand/or other KPIs between fog nodes, regional nodes, and the cloud.

The edge ingestion unit 247 ingests real-time data from the edgedevices. In an embodiment, the edge ingestion unit includes a processorand a memory for data filtering and/or data encoding or decoding.

In this embodiment, the fog node also includes a local controller 248that applies control messages received from regional controllers (orderives control decisions) to manage the components of the fog node. Forexample, if memory is limited in the fog node, the local controller canconfigure the SDN router to send delay-sensitive information to theclosest fog node or regional node for storage. The local controller canallocate resources to minimize processing delays and local powerconsumption. In an embodiment, the local controller can also manage dataanonymization. In a further embodiment, the local controller can controlthe local measurement agent to ensure performance of measurement tasks.

To reduce overall latency, it is desirable to: (1) reduce requiredcomputations; (2) place data as close as possible to computingcomponents, and (3) reduce communication latencies between edge nodes,regional nodes, and the cloud. In various embodiments, the service node213-1 accordingly applies the following optimizations at the cloud:

API query optimization: To reduce the end-to-end processing latency,computations across multiple nearby edge and regional nodes can beminimized. Specifically, the service node runs an API query optimizationengine 216 that analyzes the computation and provides a mapping betweenthe sub-tasks that to be performed across multiple hosts from differentedge, regional, and/or central nodes.

Data placement optimization: The data placement optimization engine 214works with the API query optimization engine 216 to identify optimallocations for storing data that is required for performing decentralizedprocessing with the lowest possible latency. In an embodiment, the dataplacement optimization engine determines, which part of the data streamis stored at which portion of the network (fog node, regional node, orcentral node).

Traffic routing optimization: The traffic routing optimization engine215 performs data routing to minimize the data communication latency foreach API registered at the service node called by an end device.Specifically, the engine 215 receives various communication andcomputation latencies among various nodes and determines how to avoidcongested routes and loaded CPUs. This optimization is performeddynamically, per edge device and per API.

FIG. 2E schematically illustrates a procedure 205 for applyingoptimization to minimize latency in two different applications. Based onthe application, a data placement optimization engine (run by a servicenode of central node 253 in the cloud) optimally places thecomplementary data at edge and regional nodes 251, 252. These data arecomplementary to the real-time data 250 received from the edge devices.The local/regional data and the real-time data from the edge devices arerequired for applying decentralized processing with the lowest possiblelatency. Therefore, as soon as the edge-device data 250 is available,they can be jointly processed with the local complementary data at localnodes and provide the minimum latency analysis.

Based on current latency measurements (and measurements of other KPIs),the traffic routing optimization engine may configure two optimalroutes: (1) a low latency route for forwarding packets of a low-latencyapplication and (2) a high-latency route for forwarding packets of ahigh-latency application.

FIG. 2F illustrates a procedure 206 including data processing for afirst application requiring relatively low latency and a secondapplication with higher latency, in accordance with an embodiment of thedisclosure. In the example shown in FIG. 2F, a face expressionrecognition (FXR) application 264 (in this embodiment, installed in avehicle to detect drowsiness in the vehicle operator) is time sensitiveand thus requires low latency, while a face recognition (FR) application265 on a smart phone is less time-sensitive and thus can tolerate higherlatency.

In this embodiment, a service node 266 of central node 263 includes aplacement engine and a traffic routing engine. The placement enginepartitions each application into three independent base applications,also referred to herein as microservices: (1) face detection (FD); (2)feature extraction (FE); and (3) face recognition (FR). The partitioningis discussed below with reference to FIG. 2G.

The placement engine places the FD microservice 264-1, 265-1, whosecomputation requirements are relatively low, at the user devices (inthis example, vehicle and smart phone respectively). The FE and FR microservice, which are more computationally expensive and require moreaccess to training data, are placed at the edge node 261, regional node262 and central node 263. For the FXR vehicle application where lowlatency is required, the placement engine places the FE microservice264-2 at the edge/fog node 261 and the FR microservice 264-3 at regionalnode 262, closer to the local real-time data than the FE and FRmicroservices 265-2, 265-3 of the higher-latency application. Thetraffic routing engine then configures optimal routes for placing dataand facilitating communications.

In an embodiment, the API database 267 stores static informationregarding each API (or application). The stored information may include(but is not limited to): (1) the type of API (simple, i.e. comprisingone base API, or complex, i.e. comprising multiple base APIs); (2) howthe API is to be decomposed into its base APIs; (3) a list of dataobjects to which the base APIs require access; (4) a list of allowedusers of the API (possibly including a third-party user); (5) quality ofservice (QoS) requirements; (6) pointers to additional informationassociated with the base APIs. The additional information may include(but is not limited to): (a) policies restricting API placement (e.g.,the API must be executed at an edge node); (b) policies restricting dataplacement; (c) computation complexity (e.g., a number of CPU cyclesand/or a number of I/O cycles); (d) bandwidth requirement (e.g., anaverage bandwidth utilization by the API). In an embodiment, thisinformation is obtained by simulating one or more executions of anapplication on various nodes of the network.

FIG. 2G is a schematic illustration 207 of partitioning and measuringlatency of an application programming interface (API). An API (orapplication) may be viewed as a set of base APIs (or base applicationfunctions). The overall latency of an API can be measured as a sum oflatencies of the base APIs. In the example 207 shown in FIG. 2G, an APIis decomposed into a set of base APIs 271-273: base API₁, base API₂, andbase API₃. If the latency of API_(i) is L_(i), then the total latency isΣL_(i), assuming that each API in the set is called sequentially andthat the input of API_(i) depends on the output of API_(i-1). As shownin FIG. 2G, the respective base APIs may access different sets 2711,2712 of data objects, D₁ and D₂.

In the example of a face recognition application, there are three baseAPIs: face detection, feature extraction, and face recognition. In anembodiment, video of a user's face is transmitted to an edge node; theedge node performs face detection and then performs feature extraction.The edge node can then transfer the feature to a database that comparesthe extracted feature with a known feature, and returns the closestmatch. The database may be stored at the regional node or at the centralnode, depending on the end-to-end latency requirements for theapplication. In this embodiment, the optimization engine determineswhether the feature extraction and database search should be performedat the edge node, regional node, or central node, so that the end-to-endlatency (that is, time from the video transmission to face recognitionof the user) meets the latency requirement in the SLA for theapplication. In a further embodiment, the application may be executed atthe UE to minimize latency.

FIG. 2H is a schematic illustration 208 of dynamic optimization oflatency in a central network node, in accordance with an embodiment ofthe disclosure. As shown in FIG. 2H, node 281 includes an API/QoSrequirements database 282, an edge/regional monitoring server 283, anoptimization engine 284 for placement of processes and/or data, and anoptimization engine 285 for traffic routing. In this embodiment, node281 also includes a modeling/prediction function 286 for performancemetrics, and a triggering function 287 for the optimization engines.

Based on current measurements of KPIs, performance metrics (e.g.available bandwidth and latencies at various nodes) are predicted; thepredicted performance metrics are analyzed and compared with applicableSLAs and QoS requirements. If the measured or predicted latency does notmeet a target latency, one or more of the optimization engines istriggered in an adaptive process. In this embodiment, optimizationtriggering is performed dynamically, in response to the analysis ofchanging KPIs. If the target latency cannot be achieved after theoptimization procedure (or a specified number of optimizationprocedures), a report is transmitted to a network administrator.

FIG. 2I is a flowchart 209 depicting an illustrative embodiment of amethod, performed at a central network node, for optimizing latency inaccordance with an embodiment of the disclosure. In step 2902, a targetlatency is calculated, based on an applicable service level agreement(SLA) 2901 and a predetermined margin; the target may thus be expressedas (SLA—Margin). Key performance indicators (KPIs) for the network aremeasured (step 2904) and performance metrics are predicted based on themeasurements (step 2906). Optimization engine(s) for process or base APIplacement, data placement, and/or traffic routing are then triggered(step 2908). If the latency does not meet the target (step 2910), theoptimization procedure may be repeated (step 2912); in an embodiment,optimization may be repeated a specified number of times. If the latencytarget is not met after optimization, a report is generated (step 2914).

Optimization to minimize latency for an application may be performedwith respect to data placement, base API placement, or both.

Optimization is performed with respect to data placement when thelocations of the application or base APIs are known. For example, a baseAPI may be known to be performed (or required to be performed) at aparticular node (that is, a fog node, regional node, or central node)based on computational complexity of the base API and/or availablecomputational resources at a given node.

Optimization is performed with respect to API placement when the datalocations are known. For example, a data object may be known to bestored (or required to be stored) at a particular location (that is, afog node, regional node, or central node) based on the size of the dataobject and available storage at a given node.

More generally, applications/APIs and data are deployed on the networkto minimize latencies of the applications/APIs, subject to variouscriteria including (1) resources used by the applications (e.g.bandwidth and/or computational resources) and (2) a target latencyreflecting requirements based on a SLA. Furthermore, the network may beoptimized with respect to a KPI in addition to, or instead of, thelatency.

FIG. 2J is a flowchart 2100 depicting an illustrative embodiment of amethod for optimizing latency with respect to data placement and/or APIplacement in a network, in accordance with various aspects describedherein.

In an embodiment, inputs 2101 to an optimization engine can include: (1)identifiers and locations of applications and jobs to be run; (2)priority of the application; (3) available bandwidth; (4) measurementdelay; (5) available computational and storage resources; (6) local andreal-time data relating to particular nodes; (7) auxiliary information,including information from sources outside the network, e.g. publicwebsites and social networks. In step 2102, an objective function isdefined that targets latency, a function of latency, or some otherobjective to satisfy requirements of the application (e.g. QoSrequirements).

If it is determined that optimization should be performed with respectto data placement (step 2104), the optimization engine finds anallocation A_(D) of data objects in edge, regional and central nodes tooptimize the objective function (step 2106). The output A_(D) representsan association of data objects with specific nodes (step 2108). Networkresources are then allocated (step 2118) to configure or reconfiguredata flow routes on the network. In an embodiment, the network routesare configured in accordance with flow rules applied across the network.

In a further embodiment, a heuristic optimization procedure may beperformed with respect to data placement, for one or more applications.The applications are sorted in increasing order of latency requirements,and the data objects are sorted in decreasing order of update frequency.Each successive data object is then placed so as to meet the objectivefunction for all base APIs that require that data object.

If it is determined that optimization should be performed with respectto API placement (step 2103), the optimization engine finds apartitioning AA of base APIs in edge, regional and central nodes tooptimize the objective function (step 2105). The output AA represents anassociation of the base APIs with specific nodes (step 2107). Networkresources are then allocated (step 2118) to configure or reconfiguredata flow routes on the network.

In a further embodiment, a heuristic optimization procedure may beperformed with respect to API placement, for one or more applications.The applications are sorted in increasing order of latency requirements;each application is then decomposed into base APIs as specified in theAPI database. The base APIs are sorted into a sequential dependencyusing a topological sort. Each successive sorted base API is then placedso as to meet the objective function for all data objects accessed bythat base API.

If it is determined that optimization should be performed with respectto both API placement and data placement (step 2111), the optimizationengine finds an allocation A_((A,D)) of the data objects and base APIsin edge, regional and central nodes to optimize the objective function(step 2113). The output A_((A,D)) represents an association of the baseAPIs and data objects with specific nodes (step 2115). Network resourcesare then allocated (step 2118) to configure or reconfigure data flowroutes on the network.

In a further embodiment, a heuristic optimization procedure may beperformed with respect to API placement and data placement, for one ormore applications. The applications are sorted in increasing order oflatency requirements. For each application, an iterative optimization isperformed in which base API partitioning and placement are optimizedassuming an initial data placement, data placement is then optimizedassuming the initial base API placement result, and so on with dataplacement and API placement optimization performed alternately. Theiteration can be terminated after a predetermined number of steps, orwhen it is found that the data placement and base API placement are notmodified relative to the previous iteration.

The data placement optimization and base API placement optimization maybe formulated and solved using various techniques includinginteger/mixed-integer optimization, constrained optimization, heuristicoptimization, etc.

While for purposes of simplicity of explanation, the respectiveprocesses are shown and described as a series of blocks in FIGS. 2I and2J, it is to be understood and appreciated that the claimed subjectmatter is not limited by the order of the blocks, as some blocks mayoccur in different orders and/or concurrently with other blocks fromwhat is depicted and described herein. Moreover, not all illustratedblocks may be required to implement the methods described herein.

Referring now to FIG. 3, a block diagram 300 is shown illustrating anexample, non-limiting embodiment of a virtualized communication networkin accordance with various aspects described herein. In particular avirtualized communication network is presented that can be used toimplement some or all of the subsystems and functions of communicationnetwork 100, the subsystems and functions of network 201, and methods209, 2100 presented in FIGS. 1, 2A, 2I, 2J, and 3. For example,virtualized communication network 300 can facilitate in whole or in partprocedures for reducing latency including placement of data objectsand/or base APIs at edge nodes, regional nodes, or central nodes of anetwork.

In particular, a cloud networking architecture is shown that leveragescloud technologies and supports rapid innovation and scalability via atransport layer 350, a virtualized network function cloud 325 and/or oneor more cloud computing environments 375. In various embodiments, thiscloud networking architecture is an open architecture that leveragesapplication programming interfaces (APIs); reduces complexity fromservices and operations; supports more nimble business models; andrapidly and seamlessly scales to meet evolving customer requirementsincluding traffic growth, diversity of traffic types, and diversity ofperformance and reliability expectations.

In contrast to traditional network elements—which are typicallyintegrated to perform a single function, the virtualized communicationnetwork employs virtual network elements (VNEs) 330, 332, 334, etc. thatperform some or all of the functions of network elements 150, 152, 154,156, etc. For example, the network architecture can provide a substrateof networking capability, often called Network Function VirtualizationInfrastructure (NFVI) or simply infrastructure that is capable of beingdirected with software and Software Defined Networking (SDN) protocolsto perform a broad variety of network functions and services. Thisinfrastructure can include several types of substrates. The most typicaltype of substrate being servers that support Network FunctionVirtualization (NFV), followed by packet forwarding capabilities basedon generic computing resources, with specialized network technologiesbrought to bear when general purpose processors or general purposeintegrated circuit devices offered by merchants (referred to herein asmerchant silicon) are not appropriate. In this case, communicationservices can be implemented as cloud-centric workloads.

As an example, a traditional network element 150 (shown in FIG. 1), suchas an edge router can be implemented via a VNE 330 composed of NFVsoftware modules, merchant silicon, and associated controllers. Thesoftware can be written so that increasing workload consumes incrementalresources from a common resource pool, and moreover so that it'selastic: so the resources are only consumed when needed. In a similarfashion, other network elements such as other routers, switches, edgecaches, and middle-boxes are instantiated from the common resource pool.Such sharing of infrastructure across a broad set of uses makes planningand growing infrastructure easier to manage.

In an embodiment, the transport layer 350 includes fiber, cable, wiredand/or wireless transport elements, network elements and interfaces toprovide broadband access 110, wireless access 120, voice access 130,media access 140 and/or access to content sources 175 for distributionof content to any or all of the access technologies. In particular, insome cases a network element needs to be positioned at a specific place,and this allows for less sharing of common infrastructure. Other times,the network elements have specific physical layer adapters that cannotbe abstracted or virtualized, and might require special DSP code andanalog front-ends (AFEs) that do not lend themselves to implementationas VNEs 330, 332 or 334. These network elements can be included intransport layer 350.

The virtualized network function cloud 325 interfaces with the transportlayer 350 to provide the VNEs 330, 332, 334, etc. to provide specificNFVs. In particular, the virtualized network function cloud 325leverages cloud operations, applications, and architectures to supportnetworking workloads. The virtualized network elements 330, 332 and 334can employ network function software that provides either a one-for-onemapping of traditional network element function or alternately somecombination of network functions designed for cloud computing. Forexample, VNEs 330, 332 and 334 can include route reflectors, domain namesystem (DNS) servers, and dynamic host configuration protocol (DHCP)servers, system architecture evolution (SAE) and/or mobility managemententity (MME) gateways, broadband network gateways, IP edge routers forIP-VPN, Ethernet and other services, load balancers, distributers andother network elements. Because these elements don't typically need toforward large amounts of traffic, their workload can be distributedacross a number of servers—each of which adds a portion of thecapability, and overall which creates an elastic function with higheravailability than its former monolithic version. These virtual networkelements 330, 332, 334, etc. can be instantiated and managed using anorchestration approach similar to those used in cloud compute services.

The cloud computing environments 375 can interface with the virtualizednetwork function cloud 325 via APIs that expose functional capabilitiesof the VNEs 330, 332, 334, etc. to provide the flexible and expandedcapabilities to the virtualized network function cloud 325. Inparticular, network workloads may have applications distributed acrossthe virtualized network function cloud 325 and cloud computingenvironment 375 and in the commercial cloud, or might simply orchestrateworkloads supported entirely in NFV infrastructure from these thirdparty locations.

Turning now to FIG. 4, there is illustrated a block diagram of acomputing environment in accordance with various aspects describedherein. In order to provide additional context for various embodimentsof the embodiments described herein, FIG. 4 and the following discussionare intended to provide a brief, general description of a suitablecomputing environment 400 in which the various embodiments of thesubject disclosure can be implemented. In particular, computingenvironment 400 can be used in the implementation of network elements150, 152, 154, 156, access terminal 112, base station or access point122, switching device 132, media terminal 142, and/or VNEs 330, 332,334, etc. Each of these devices can be implemented viacomputer-executable instructions that can run on one or more computers,and/or in combination with other program modules and/or as a combinationof hardware and software. For example, computing environment 400 canfacilitate in whole or in part dynamically assigning resources for dataprocessing so that a latency associated therewith meets a latencycriterion; the resources can comprise computation resources and storageresources of one or more of a network edge node, a regional node of aplurality of regional nodes in communication with the edge node and acentral node, and the central node.

Generally, program modules comprise routines, programs, components, datastructures, etc., that perform particular tasks or implement particularabstract data types. Moreover, those skilled in the art will appreciatethat the inventive methods can be practiced with other computer systemconfigurations, comprising single-processor or multiprocessor computersystems, minicomputers, mainframe computers, as well as personalcomputers, hand-held computing devices, microprocessor-based orprogrammable consumer electronics, and the like, each of which can beoperatively coupled to one or more associated devices.

As used herein, a processing circuit includes one or more processors aswell as other application specific circuits such as an applicationspecific integrated circuit, digital logic circuit, state machine,programmable gate array or other circuit that processes input signals ordata and that produces output signals or data in response thereto. Itshould be noted that while any functions and features described hereinin association with the operation of a processor could likewise beperformed by a processing circuit.

The illustrated embodiments of the embodiments herein can be alsopracticed in distributed computing environments where certain tasks areperformed by remote processing devices that are linked through acommunications network. In a distributed computing environment, programmodules can be located in both local and remote memory storage devices.

Computing devices typically comprise a variety of media, which cancomprise computer-readable storage media and/or communications media,which two terms are used herein differently from one another as follows.Computer-readable storage media can be any available storage media thatcan be accessed by the computer and comprises both volatile andnonvolatile media, removable and non-removable media. By way of example,and not limitation, computer-readable storage media can be implementedin connection with any method or technology for storage of informationsuch as computer-readable instructions, program modules, structured dataor unstructured data.

Computer-readable storage media can comprise, but are not limited to,random access memory (RAM), read only memory (ROM), electricallyerasable programmable read only memory (EEPROM), flash memory or othermemory technology, compact disk read only memory (CD-ROM), digitalversatile disk (DVD) or other optical disk storage, magnetic cassettes,magnetic tape, magnetic disk storage or other magnetic storage devicesor other tangible and/or non-transitory media which can be used to storedesired information. In this regard, the terms “tangible” or“non-transitory” herein as applied to storage, memory orcomputer-readable media, are to be understood to exclude onlypropagating transitory signals per se as modifiers and do not relinquishrights to all standard storage, memory or computer-readable media thatare not only propagating transitory signals per se.

Computer-readable storage media can be accessed by one or more local orremote computing devices, e.g., via access requests, queries or otherdata retrieval protocols, for a variety of operations with respect tothe information stored by the medium.

Communications media typically embody computer-readable instructions,data structures, program modules or other structured or unstructureddata in a data signal such as a modulated data signal, e.g., a carrierwave or other transport mechanism, and comprises any informationdelivery or transport media. The term “modulated data signal” or signalsrefers to a signal that has one or more of its characteristics set orchanged in such a manner as to encode information in one or moresignals. By way of example, and not limitation, communication mediacomprise wired media, such as a wired network or direct-wiredconnection, and wireless media such as acoustic, RF, infrared and otherwireless media.

With reference again to FIG. 4, the example environment can comprise acomputer 402, the computer 402 comprising a processing unit 404, asystem memory 406 and a system bus 408. The system bus 408 couplessystem components including, but not limited to, the system memory 406to the processing unit 404. The processing unit 404 can be any ofvarious commercially available processors. Dual microprocessors andother multiprocessor architectures can also be employed as theprocessing unit 404.

The system bus 408 can be any of several types of bus structure that canfurther interconnect to a memory bus (with or without a memorycontroller), a peripheral bus, and a local bus using any of a variety ofcommercially available bus architectures. The system memory 406comprises ROM 410 and RAM 412. A basic input/output system (BIOS) can bestored in a non-volatile memory such as ROM, erasable programmable readonly memory (EPROM), EEPROM, which BIOS contains the basic routines thathelp to transfer information between elements within the computer 402,such as during startup. The RAM 412 can also comprise a high-speed RAMsuch as static RAM for caching data.

The computer 402 further comprises an internal hard disk drive (HDD) 414(e.g., EIDE, SATA), which internal HDD 414 can also be configured forexternal use in a suitable chassis (not shown), a magnetic floppy diskdrive (FDD) 416, (e.g., to read from or write to a removable diskette418) and an optical disk drive 420, (e.g., reading a CD-ROM disk 422 or,to read from or write to other high capacity optical media such as theDVD). The HDD 414, magnetic FDD 416 and optical disk drive 420 can beconnected to the system bus 408 by a hard disk drive interface 424, amagnetic disk drive interface 426 and an optical drive interface 428,respectively. The hard disk drive interface 424 for external driveimplementations comprises at least one or both of Universal Serial Bus(USB) and Institute of Electrical and Electronics Engineers (IEEE) 1394interface technologies. Other external drive connection technologies arewithin contemplation of the embodiments described herein.

The drives and their associated computer-readable storage media providenonvolatile storage of data, data structures, computer-executableinstructions, and so forth. For the computer 402, the drives and storagemedia accommodate the storage of any data in a suitable digital format.Although the description of computer-readable storage media above refersto a hard disk drive (HDD), a removable magnetic diskette, and aremovable optical media such as a CD or DVD, it should be appreciated bythose skilled in the art that other types of storage media which arereadable by a computer, such as zip drives, magnetic cassettes, flashmemory cards, cartridges, and the like, can also be used in the exampleoperating environment, and further, that any such storage media cancontain computer-executable instructions for performing the methodsdescribed herein.

A number of program modules can be stored in the drives and RAM 412,comprising an operating system 430, one or more application programs432, other program modules 434 and program data 436. All or portions ofthe operating system, applications, modules, and/or data can also becached in the RAM 412. The systems and methods described herein can beimplemented utilizing various commercially available operating systemsor combinations of operating systems.

A user can enter commands and information into the computer 402 throughone or more wired/wireless input devices, e.g., a keyboard 438 and apointing device, such as a mouse 440. Other input devices (not shown)can comprise a microphone, an infrared (IR) remote control, a joystick,a game pad, a stylus pen, touch screen or the like. These and otherinput devices are often connected to the processing unit 404 through aninput device interface 442 that can be coupled to the system bus 408,but can be connected by other interfaces, such as a parallel port, anIEEE 1394 serial port, a game port, a universal serial bus (USB) port,an IR interface, etc.

A monitor 444 or other type of display device can be also connected tothe system bus 408 via an interface, such as a video adapter 446. Itwill also be appreciated that in alternative embodiments, a monitor 444can also be any display device (e.g., another computer having a display,a smart phone, a tablet computer, etc.) for receiving displayinformation associated with computer 402 via any communication means,including via the Internet and cloud-based networks. In addition to themonitor 444, a computer typically comprises other peripheral outputdevices (not shown), such as speakers, printers, etc.

The computer 402 can operate in a networked environment using logicalconnections via wired and/or wireless communications to one or moreremote computers, such as a remote computer(s) 448. The remotecomputer(s) 448 can be a workstation, a server computer, a router, apersonal computer, portable computer, microprocessor-based entertainmentappliance, a peer device or other common network node, and typicallycomprises many or all of the elements described relative to the computer402, although, for purposes of brevity, only a remote memory/storagedevice 450 is illustrated. The logical connections depicted comprisewired/wireless connectivity to a local area network (LAN) 452 and/orlarger networks, e.g., a wide area network (WAN) 454. Such LAN and WANnetworking environments are commonplace in offices and companies, andfacilitate enterprise-wide computer networks, such as intranets, all ofwhich can connect to a global communications network, e.g., theInternet.

When used in a LAN networking environment, the computer 402 can beconnected to the LAN 452 through a wired and/or wireless communicationnetwork interface or adapter 456. The adapter 456 can facilitate wiredor wireless communication to the LAN 452, which can also comprise awireless AP disposed thereon for communicating with the adapter 456.

When used in a WAN networking environment, the computer 402 can comprisea modem 458 or can be connected to a communications server on the WAN454 or has other means for establishing communications over the WAN 454,such as by way of the Internet. The modem 458, which can be internal orexternal and a wired or wireless device, can be connected to the systembus 408 via the input device interface 442. In a networked environment,program modules depicted relative to the computer 402 or portionsthereof, can be stored in the remote memory/storage device 450. It willbe appreciated that the network connections shown are example and othermeans of establishing a communications link between the computers can beused.

The computer 402 can be operable to communicate with any wirelessdevices or entities operatively disposed in wireless communication,e.g., a printer, scanner, desktop and/or portable computer, portabledata assistant, communications satellite, any piece of equipment orlocation associated with a wirelessly detectable tag (e.g., a kiosk,news stand, restroom), and telephone. This can comprise WirelessFidelity (Wi-Fi) and BLUETOOTH® wireless technologies. Thus, thecommunication can be a predefined structure as with a conventionalnetwork or simply an ad hoc communication between at least two devices.

Wi-Fi can allow connection to the Internet from a couch at home, a bedin a hotel room or a conference room at work, without wires. Wi-Fi is awireless technology similar to that used in a cell phone that enablessuch devices, e.g., computers, to send and receive data indoors and out;anywhere within the range of a base station. Wi-Fi networks use radiotechnologies called IEEE 802.11 (a, b, g, n, ac, ag, etc.) to providesecure, reliable, fast wireless connectivity. A Wi-Fi network can beused to connect computers to each other, to the Internet, and to wirednetworks (which can use IEEE 802.3 or Ethernet). Wi-Fi networks operatein the unlicensed 2.4 and 5 GHz radio bands for example or with productsthat contain both bands (dual band), so the networks can providereal-world performance similar to the basic 10BaseT wired Ethernetnetworks used in many offices.

Turning now to FIG. 5, an embodiment 500 of a mobile network platform510 is shown that is an example of network elements 150, 152, 154, 156,and/or VNEs 330, 332, 334, etc. For example, platform 510 can facilitatein whole or in part receiving data at an edge node of a network thatalso includes regional nodes and central nodes, and determining alatency criterion associated with an application for processing thedata, where the application corresponds to an application programminginterface (API) included in a database accessible to a central node. Inone or more embodiments, the mobile network platform 510 can generateand receive signals transmitted and received by base stations or accesspoints such as base station or access point 122. Generally, mobilenetwork platform 510 can comprise components, e.g., nodes, gateways,interfaces, servers, or disparate platforms, that facilitate bothpacket-switched (PS) (e.g., internet protocol (IP), frame relay,asynchronous transfer mode (ATM)) and circuit-switched (CS) traffic(e.g., voice and data), as well as control generation for networkedwireless telecommunication. As a non-limiting example, mobile networkplatform 510 can be included in telecommunications carrier networks, andcan be considered carrier-side components as discussed elsewhere herein.Mobile network platform 510 comprises CS gateway node(s) 512 which caninterface CS traffic received from legacy networks like telephonynetwork(s) 540 (e.g., public switched telephone network (PSTN), orpublic land mobile network (PLMN)) or a signaling system #7 (SS7)network 560. CS gateway node(s) 512 can authorize and authenticatetraffic (e.g., voice) arising from such networks. Additionally, CSgateway node(s) 512 can access mobility, or roaming, data generatedthrough SS7 network 560; for instance, mobility data stored in a visitedlocation register (VLR), which can reside in memory 530. Moreover, CSgateway node(s) 512 interfaces CS-based traffic and signaling and PSgateway node(s) 518. As an example, in a 3GPP UMTS network, CS gatewaynode(s) 512 can be realized at least in part in gateway GPRS supportnode(s) (GGSN). It should be appreciated that functionality and specificoperation of CS gateway node(s) 512, PS gateway node(s) 518, and servingnode(s) 516, is provided and dictated by radio technology(ies) utilizedby mobile network platform 510 for telecommunication over a radio accessnetwork 520 with other devices, such as a radiotelephone 575.

In addition to receiving and processing CS-switched traffic andsignaling, PS gateway node(s) 518 can authorize and authenticatePS-based data sessions with served mobile devices. Data sessions cancomprise traffic, or content(s), exchanged with networks external to themobile network platform 510, like wide area network(s) (WANs) 550,enterprise network(s) 570, and service network(s) 580, which can beembodied in local area network(s) (LANs), can also be interfaced withmobile network platform 510 through PS gateway node(s) 518. It is to benoted that WANs 550 and enterprise network(s) 570 can embody, at leastin part, a service network(s) like IP multimedia subsystem (IMS). Basedon radio technology layer(s) available in technology resource(s) orradio access network 520, PS gateway node(s) 518 can generate packetdata protocol contexts when a data session is established; other datastructures that facilitate routing of packetized data also can begenerated. To that end, in an aspect, PS gateway node(s) 518 cancomprise a tunnel interface (e.g., tunnel termination gateway (TTG) in3GPP UMTS network(s) (not shown)) which can facilitate packetizedcommunication with disparate wireless network(s), such as Wi-Finetworks.

In embodiment 500, mobile network platform 510 also comprises servingnode(s) 516 that, based upon available radio technology layer(s) withintechnology resource(s) in the radio access network 520, convey thevarious packetized flows of data streams received through PS gatewaynode(s) 518. It is to be noted that for technology resource(s) that relyprimarily on CS communication, server node(s) can deliver trafficwithout reliance on PS gateway node(s) 518; for example, server node(s)can embody at least in part a mobile switching center. As an example, ina 3GPP UMTS network, serving node(s) 516 can be embodied in serving GPRSsupport node(s) (SGSN).

For radio technologies that exploit packetized communication, server(s)514 in mobile network platform 510 can execute numerous applicationsthat can generate multiple disparate packetized data streams or flows,and manage (e.g., schedule, queue, format . . . ) such flows. Suchapplication(s) can comprise add-on features to standard services (forexample, provisioning, billing, customer support . . . ) provided bymobile network platform 510. Data streams (e.g., content(s) that arepart of a voice call or data session) can be conveyed to PS gatewaynode(s) 518 for authorization/authentication and initiation of a datasession, and to serving node(s) 516 for communication thereafter. Inaddition to application server, server(s) 514 can comprise utilityserver(s), a utility server can comprise a provisioning server, anoperations and maintenance server, a security server that can implementat least in part a certificate authority and firewalls as well as othersecurity mechanisms, and the like. In an aspect, security server(s)secure communication served through mobile network platform 510 toensure network's operation and data integrity in addition toauthorization and authentication procedures that CS gateway node(s) 512and PS gateway node(s) 518 can enact. Moreover, provisioning server(s)can provision services from external network(s) like networks operatedby a disparate service provider; for instance, WAN 550 or GlobalPositioning System (GPS) network(s) (not shown). Provisioning server(s)can also provision coverage through networks associated to mobilenetwork platform 510 (e.g., deployed and operated by the same serviceprovider), such as the distributed antennas networks shown in FIG. 1(s)that enhance wireless service coverage by providing more networkcoverage.

It is to be noted that server(s) 514 can comprise one or more processorsconfigured to confer at least in part the functionality of mobilenetwork platform 510. To that end, the one or more processor can executecode instructions stored in memory 530, for example. It is should beappreciated that server(s) 514 can comprise a content manager, whichoperates in substantially the same manner as described hereinbefore.

In example embodiment 500, memory 530 can store information related tooperation of mobile network platform 510. Other operational informationcan comprise provisioning information of mobile devices served throughmobile network platform 510, subscriber databases; applicationintelligence, pricing schemes, e.g., promotional rates, flat-rateprograms, couponing campaigns; technical specification(s) consistentwith telecommunication protocols for operation of disparate radio, orwireless, technology layers; and so forth. Memory 530 can also storeinformation from at least one of telephony network(s) 540, WAN 550, SS7network 560, or enterprise network(s) 570. In an aspect, memory 530 canbe, for example, accessed as part of a data store component or as aremotely connected memory store.

In order to provide a context for the various aspects of the disclosedsubject matter, FIG. 5, and the following discussion, are intended toprovide a brief, general description of a suitable environment in whichthe various aspects of the disclosed subject matter can be implemented.While the subject matter has been described above in the general contextof computer-executable instructions of a computer program that runs on acomputer and/or computers, those skilled in the art will recognize thatthe disclosed subject matter also can be implemented in combination withother program modules. Generally, program modules comprise routines,programs, components, data structures, etc. that perform particulartasks and/or implement particular abstract data types.

Turning now to FIG. 6, an illustrative embodiment of a communicationdevice 600 is shown. The communication device 600 can serve as anillustrative embodiment of devices such as data terminals 114, mobiledevices 124, vehicle 126, display devices 144 or other client devicesfor communication via either communications network 125. For example,computing device 600 can facilitate in whole or in part receiving dataat an edge node of a network that also includes regional nodes andcentral nodes, and determining a latency criterion associated with anapplication for processing the data, where the application correspondsto an application programming interface (API) included in a databaseaccessible to a central node.

The communication device 600 can comprise a wireline and/or wirelesstransceiver 602 (herein transceiver 602), a user interface (UI) 604, apower supply 614, a location receiver 616, a motion sensor 618, anorientation sensor 620, and a controller 606 for managing operationsthereof. The transceiver 602 can support short-range or long-rangewireless access technologies such as Bluetooth®, ZigBee®, WiFi, DECT, orcellular communication technologies, just to mention a few (Bluetooth®and ZigBee® are trademarks registered by the Bluetooth® Special InterestGroup and the ZigBee® Alliance, respectively). Cellular technologies caninclude, for example, CDMA-1×, UMTS/HSDPA, GSM/GPRS, TDMA/EDGE, EV/DO,WiMAX, SDR, LTE, as well as other next generation wireless communicationtechnologies as they arise. The transceiver 602 can also be adapted tosupport circuit-switched wireline access technologies (such as PSTN),packet-switched wireline access technologies (such as TCP/IP, VoIP,etc.), and combinations thereof.

The UI 604 can include a depressible or touch-sensitive keypad 608 witha navigation mechanism such as a roller ball, a joystick, a mouse, or anavigation disk for manipulating operations of the communication device600. The keypad 608 can be an integral part of a housing assembly of thecommunication device 600 or an independent device operably coupledthereto by a tethered wireline interface (such as a USB cable) or awireless interface supporting for example Bluetooth®. The keypad 608 canrepresent a numeric keypad commonly used by phones, and/or a QWERTYkeypad with alphanumeric keys. The UI 604 can further include a display610 such as monochrome or color LCD (Liquid Crystal Display), OLED(Organic Light Emitting Diode) or other suitable display technology forconveying images to an end user of the communication device 600. In anembodiment where the display 610 is touch-sensitive, a portion or all ofthe keypad 608 can be presented by way of the display 610 withnavigation features.

The display 610 can use touch screen technology to also serve as a userinterface for detecting user input. As a touch screen display, thecommunication device 600 can be adapted to present a user interfacehaving graphical user interface (GUI) elements that can be selected by auser with a touch of a finger. The display 610 can be equipped withcapacitive, resistive or other forms of sensing technology to detect howmuch surface area of a user's finger has been placed on a portion of thetouch screen display. This sensing information can be used to controlthe manipulation of the GUI elements or other functions of the userinterface. The display 610 can be an integral part of the housingassembly of the communication device 600 or an independent devicecommunicatively coupled thereto by a tethered wireline interface (suchas a cable) or a wireless interface.

The UI 604 can also include an audio system 612 that utilizes audiotechnology for conveying low volume audio (such as audio heard inproximity of a human ear) and high volume audio (such as speakerphonefor hands free operation). The audio system 612 can further include amicrophone for receiving audible signals of an end user. The audiosystem 612 can also be used for voice recognition applications. The UI604 can further include an image sensor 613 such as a charged coupleddevice (CCD) camera for capturing still or moving images.

The power supply 614 can utilize common power management technologiessuch as replaceable and rechargeable batteries, supply regulationtechnologies, and/or charging system technologies for supplying energyto the components of the communication device 600 to facilitatelong-range or short-range portable communications. Alternatively, or incombination, the charging system can utilize external power sources suchas DC power supplied over a physical interface such as a USB port orother suitable tethering technologies.

The location receiver 616 can utilize location technology such as aglobal positioning system (GPS) receiver capable of assisted GPS foridentifying a location of the communication device 600 based on signalsgenerated by a constellation of GPS satellites, which can be used forfacilitating location services such as navigation. The motion sensor 618can utilize motion sensing technology such as an accelerometer, agyroscope, or other suitable motion sensing technology to detect motionof the communication device 600 in three-dimensional space. Theorientation sensor 620 can utilize orientation sensing technology suchas a magnetometer to detect the orientation of the communication device600 (north, south, west, and east, as well as combined orientations indegrees, minutes, or other suitable orientation metrics).

The communication device 600 can use the transceiver 602 to alsodetermine a proximity to a cellular, WiFi, Bluetooth®, or other wirelessaccess points by sensing techniques such as utilizing a received signalstrength indicator (RSSI) and/or signal time of arrival (TOA) or time offlight (TOF) measurements. The controller 606 can utilize computingtechnologies such as a microprocessor, a digital signal processor (DSP),programmable gate arrays, application specific integrated circuits,and/or a video processor with associated storage memory such as Flash,ROM, RAM, SRAM, DRAM or other storage technologies for executingcomputer instructions, controlling, and processing data supplied by theaforementioned components of the communication device 600.

Other components not shown in FIG. 6 can be used in one or moreembodiments of the subject disclosure. For instance, the communicationdevice 600 can include a slot for adding or removing an identity modulesuch as a Subscriber Identity Module (SIM) card or Universal IntegratedCircuit Card (UICC). SIM or UICC cards can be used for identifyingsubscriber services, executing programs, storing subscriber data, and soon.

The terms “first,” “second,” “third,” and so forth, as used in theclaims, unless otherwise clear by context, is for clarity only anddoesn't otherwise indicate or imply any order in time. For instance, “afirst determination,” “a second determination,” and “a thirddetermination,” does not indicate or imply that the first determinationis to be made before the second determination, or vice versa, etc.

In the subject specification, terms such as “store,” “storage,” “datastore,” data storage,” “database,” and substantially any otherinformation storage component relevant to operation and functionality ofa component, refer to “memory components,” or entities embodied in a“memory” or components comprising the memory. It will be appreciatedthat the memory components described herein can be either volatilememory or nonvolatile memory, or can comprise both volatile andnonvolatile memory, by way of illustration, and not limitation, volatilememory, non-volatile memory, disk storage, and memory storage. Further,nonvolatile memory can be included in read only memory (ROM),programmable ROM (PROM), electrically programmable ROM (EPROM),electrically erasable ROM (EEPROM), or flash memory. Volatile memory cancomprise random access memory (RAM), which acts as external cachememory. By way of illustration and not limitation, RAM is available inmany forms such as synchronous RAM (SRAM), dynamic RAM (DRAM),synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhancedSDRAM (ESDRAM), Synchlink DRAM (SLDRAM), and direct Rambus RAM (DRRAM).Additionally, the disclosed memory components of systems or methodsherein are intended to comprise, without being limited to comprising,these and any other suitable types of memory.

Moreover, it will be noted that the disclosed subject matter can bepracticed with other computer system configurations, comprisingsingle-processor or multiprocessor computer systems, mini-computingdevices, mainframe computers, as well as personal computers, hand-heldcomputing devices (e.g., PDA, phone, smartphone, watch, tabletcomputers, netbook computers, etc.), microprocessor-based orprogrammable consumer or industrial electronics, and the like. Theillustrated aspects can also be practiced in distributed computingenvironments where tasks are performed by remote processing devices thatare linked through a communications network; however, some if not allaspects of the subject disclosure can be practiced on stand-alonecomputers. In a distributed computing environment, program modules canbe located in both local and remote memory storage devices.

In one or more embodiments, information regarding use of services can begenerated including services being accessed, media consumption history,user preferences, and so forth. This information can be obtained byvarious methods including user input, detecting types of communications(e.g., video content vs. audio content), analysis of content streams,sampling, and so forth. The generating, obtaining and/or monitoring ofthis information can be responsive to an authorization provided by theuser. In one or more embodiments, an analysis of data can be subject toauthorization from user(s) associated with the data, such as an opt-in,an opt-out, acknowledgement requirements, notifications, selectiveauthorization based on types of data, and so forth.

Some of the embodiments described herein can also employ artificialintelligence (AI) to facilitate automating one or more featuresdescribed herein. The embodiments (e.g., in connection withautomatically identifying acquired cell sites that provide a maximumvalue/benefit after addition to an existing communication network) canemploy various AI-based schemes for carrying out various embodimentsthereof. Moreover, the classifier can be employed to determine a rankingor priority of each cell site of the acquired network. A classifier is afunction that maps an input attribute vector, x=(x1, x2, x3, x4, . . . ,xn), to a confidence that the input belongs to a class, that is,f(x)=confidence (class). Such classification can employ a probabilisticand/or statistical-based analysis (e.g., factoring into the analysisutilities and costs) to determine or infer an action that a user desiresto be automatically performed. A support vector machine (SVM) is anexample of a classifier that can be employed. The SVM operates byfinding a hypersurface in the space of possible inputs, which thehypersurface attempts to split the triggering criteria from thenon-triggering events. Intuitively, this makes the classificationcorrect for testing data that is near, but not identical to trainingdata. Other directed and undirected model classification approachescomprise, e.g., naïve Bayes, Bayesian networks, decision trees, neuralnetworks, fuzzy logic models, and probabilistic classification modelsproviding different patterns of independence can be employed.Classification as used herein also is inclusive of statisticalregression that is utilized to develop models of priority.

As will be readily appreciated, one or more of the embodiments canemploy classifiers that are explicitly trained (e.g., via a generictraining data) as well as implicitly trained (e.g., via observing UEbehavior, operator preferences, historical information, receivingextrinsic information). For example, SVMs can be configured via alearning or training phase within a classifier constructor and featureselection module. Thus, the classifier(s) can be used to automaticallylearn and perform a number of functions, including but not limited todetermining according to predetermined criteria which of the acquiredcell sites will benefit a maximum number of subscribers and/or which ofthe acquired cell sites will add minimum value to the existingcommunication network coverage, etc.

As used in some contexts in this application, in some embodiments, theterms “component,” “system” and the like are intended to refer to, orcomprise, a computer-related entity or an entity related to anoperational apparatus with one or more specific functionalities, whereinthe entity can be either hardware, a combination of hardware andsoftware, software, or software in execution. As an example, a componentmay be, but is not limited to being, a process running on a processor, aprocessor, an object, an executable, a thread of execution,computer-executable instructions, a program, and/or a computer. By wayof illustration and not limitation, both an application running on aserver and the server can be a component. One or more components mayreside within a process and/or thread of execution and a component maybe localized on one computer and/or distributed between two or morecomputers. In addition, these components can execute from variouscomputer readable media having various data structures stored thereon.The components may communicate via local and/or remote processes such asin accordance with a signal having one or more data packets (e.g., datafrom one component interacting with another component in a local system,distributed system, and/or across a network such as the Internet withother systems via the signal). As another example, a component can be anapparatus with specific functionality provided by mechanical partsoperated by electric or electronic circuitry, which is operated by asoftware or firmware application executed by a processor, wherein theprocessor can be internal or external to the apparatus and executes atleast a part of the software or firmware application. As yet anotherexample, a component can be an apparatus that provides specificfunctionality through electronic components without mechanical parts,the electronic components can comprise a processor therein to executesoftware or firmware that confers at least in part the functionality ofthe electronic components. While various components have beenillustrated as separate components, it will be appreciated that multiplecomponents can be implemented as a single component, or a singlecomponent can be implemented as multiple components, without departingfrom example embodiments.

Further, the various embodiments can be implemented as a method,apparatus or article of manufacture using standard programming and/orengineering techniques to produce software, firmware, hardware or anycombination thereof to control a computer to implement the disclosedsubject matter. The term “article of manufacture” as used herein isintended to encompass a computer program accessible from anycomputer-readable device or computer-readable storage/communicationsmedia. For example, computer readable storage media can include, but arenot limited to, magnetic storage devices (e.g., hard disk, floppy disk,magnetic strips), optical disks (e.g., compact disk (CD), digitalversatile disk (DVD)), smart cards, and flash memory devices (e.g.,card, stick, key drive). Of course, those skilled in the art willrecognize many modifications can be made to this configuration withoutdeparting from the scope or spirit of the various embodiments.

In addition, the words “example” and “exemplary” are used herein to meanserving as an instance or illustration. Any embodiment or designdescribed herein as “example” or “exemplary” is not necessarily to beconstrued as preferred or advantageous over other embodiments ordesigns. Rather, use of the word example or exemplary is intended topresent concepts in a concrete fashion. As used in this application, theterm “or” is intended to mean an inclusive “or” rather than an exclusive“or”. That is, unless specified otherwise or clear from context, “Xemploys A or B” is intended to mean any of the natural inclusivepermutations. That is, if X employs A; X employs B; or X employs both Aand B, then “X employs A or B” is satisfied under any of the foregoinginstances. In addition, the articles “a” and “an” as used in thisapplication and the appended claims should generally be construed tomean “one or more” unless specified otherwise or clear from context tobe directed to a singular form.

Moreover, terms such as “user equipment,” “mobile station,” “mobile,”subscriber station,” “access terminal,” “terminal,” “handset,” “mobiledevice” (and/or terms representing similar terminology) can refer to awireless device utilized by a subscriber or user of a wirelesscommunication service to receive or convey data, control, voice, video,sound, gaming or substantially any data-stream or signaling-stream. Theforegoing terms are utilized interchangeably herein and with referenceto the related drawings.

Furthermore, the terms “user,” “subscriber,” “customer,” “consumer” andthe like are employed interchangeably throughout, unless contextwarrants particular distinctions among the terms. It should beappreciated that such terms can refer to human entities or automatedcomponents supported through artificial intelligence (e.g., a capacityto make inference based, at least, on complex mathematical formalisms),which can provide simulated vision, sound recognition and so forth.

As employed herein, the term “processor” can refer to substantially anycomputing processing unit or device comprising, but not limited tocomprising, single-core processors; single-processors with softwaremultithread execution capability; multi-core processors; multi-coreprocessors with software multithread execution capability; multi-coreprocessors with hardware multithread technology; parallel platforms; andparallel platforms with distributed shared memory. Additionally, aprocessor can refer to an integrated circuit, an application specificintegrated circuit (ASIC), a digital signal processor (DSP), a fieldprogrammable gate array (FPGA), a programmable logic controller (PLC), acomplex programmable logic device (CPLD), a discrete gate or transistorlogic, discrete hardware components or any combination thereof designedto perform the functions described herein. Processors can exploitnano-scale architectures such as, but not limited to, molecular andquantum-dot based transistors, switches and gates, in order to optimizespace usage or enhance performance of user equipment. A processor canalso be implemented as a combination of computing processing units.

As used herein, terms such as “data storage,” data storage,” “database,”and substantially any other information storage component relevant tooperation and functionality of a component, refer to “memorycomponents,” or entities embodied in a “memory” or components comprisingthe memory. It will be appreciated that the memory components orcomputer-readable storage media, described herein can be either volatilememory or nonvolatile memory or can include both volatile andnonvolatile memory.

What has been described above includes mere examples of variousembodiments. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing these examples, but one of ordinary skill in the art canrecognize that many further combinations and permutations of the presentembodiments are possible. Accordingly, the embodiments disclosed and/orclaimed herein are intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the term “includes”is used in either the detailed description or the claims, such term isintended to be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

In addition, a flow diagram may include a “start” and/or “continue”indication. The “start” and “continue” indications reflect that thesteps presented can optionally be incorporated in or otherwise used inconjunction with other routines. In this context, “start” indicates thebeginning of the first step presented and may be preceded by otheractivities not specifically shown. Further, the “continue” indicationreflects that the steps presented may be performed multiple times and/ormay be succeeded by other activities not specifically shown. Further,while a flow diagram indicates a particular ordering of steps, otherorderings are likewise possible provided that the principles ofcausality are maintained.

As may also be used herein, the term(s) “operably coupled to”, “coupledto”, and/or “coupling” includes direct coupling between items and/orindirect coupling between items via one or more intervening items. Suchitems and intervening items include, but are not limited to, junctions,communication paths, components, circuit elements, circuits, functionalblocks, and/or devices. As an example of indirect coupling, a signalconveyed from a first item to a second item may be modified by one ormore intervening items by modifying the form, nature or format ofinformation in a signal, while one or more elements of the informationin the signal are nevertheless conveyed in a manner than can berecognized by the second item. In a further example of indirectcoupling, an action in a first item can cause a reaction on the seconditem, as a result of actions and/or reactions in one or more interveningitems.

Although specific embodiments have been illustrated and describedherein, it should be appreciated that any arrangement which achieves thesame or similar purpose may be substituted for the embodiments describedor shown by the subject disclosure. The subject disclosure is intendedto cover any and all adaptations or variations of various embodiments.Combinations of the above embodiments, and other embodiments notspecifically described herein, can be used in the subject disclosure.For instance, one or more features from one or more embodiments can becombined with one or more features of one or more other embodiments. Inone or more embodiments, features that are positively recited can alsobe negatively recited and excluded from the embodiment with or withoutreplacement by another structural and/or functional feature. The stepsor functions described with respect to the embodiments of the subjectdisclosure can be performed in any order. The steps or functionsdescribed with respect to the embodiments of the subject disclosure canbe performed alone or in combination with other steps or functions ofthe subject disclosure, as well as from other embodiments or from othersteps that have not been described in the subject disclosure. Further,more than or less than all of the features described with respect to anembodiment can also be utilized.

What is claimed is:
 1. A method comprising: receiving, by a processingsystem including a processor, data at an edge node of a plurality ofedge nodes of a network, the network further comprising a plurality ofregional nodes and a plurality of central nodes; determining, by theprocessing system, a latency criterion associated with an applicationfor processing the data, the application utilizing an applicationprogramming interface (API), information regarding the API being storedin a database accessible to at least one of the edge node, a regionalnode of the plurality of regional nodes in communication with the edgenode, and a central node of the plurality of central nodes incommunication with the regional node; monitoring, by the processingsystem, a latency associated with the processing of the data by theapplication; determining, by the processing system, whether the latencysatisfies the latency criterion; responsive to the latency satisfyingthe latency criterion, assigning, by the processing system, resources toassist the application in reducing the latency associated with theprocessing of the data, the resources comprising computation resources,storage resources, network resources, or a combination thereof obtainedfrom the central node, the regional node, the edge node, or anycombination thereof partitioning, by the processing system, the API intoa plurality of base APIs including a first base API and a second baseAPI, wherein the first base API has associated therewith a first latencyless than a second latency associated with the second base API; andassigning, by the processing system, the edge node to the first base APIand the regional node to the second base API.
 2. The method of claim 1,wherein the API is accessible to a third party.
 3. The method of claim1, further comprising: by the processing system, measuring a keyperformance indicator (KPI) of the network, accessing the KPI, or both;and predicting, by the processing system, a network performance metricbased on the KPI.
 4. The method of claim 1, wherein the API comprises aplurality of base APIs, and wherein the database comprises a list ofdata objects to be accessed via the respective base APIs.
 5. The methodof claim 4, further comprising triggering, by the processing system, aprocedure for reducing the latency comprising placement of the dataobjects at one or more of the edge node, the regional node, and thecentral node.
 6. The method of claim 4, further comprising triggering,by the processing system, a procedure for reducing the latencycomprising placement of the base APIs at one or more of the edge node,the regional node, and the central node.
 7. The method of claim 4,further comprising triggering, by the processing system, an iterativeprocedure for reducing the latency comprising placement of the dataobjects and the base APIs at one or more of the edge node, the regionalnode, and the central node.
 8. The method of claim 1, wherein the datais streamed from the edge node to the regional node for processing atthe regional node.
 9. The method of claim 1, wherein the received datais accessible to a third party from the edge node, the regional node,the central node or a combination thereof.
 10. A device comprising: aprocessing system including a processor; and a memory that storesexecutable instructions that, when executed by the processing system,facilitate performance of operations comprising: receiving data at anedge node of a plurality of edge nodes of a network, the network furthercomprising a plurality of regional nodes and a plurality of centralnodes; determining a latency criterion associated with an applicationfor processing the data, the application utilizing an applicationprogramming interface (API), information regarding the API being storedin a database accessible to at least one of the edge node, a regionalnode of the plurality of regional nodes in communication with the edgenode, and a central node of the plurality of central nodes incommunication with the regional node; measuring a key performanceindicator (KPI) of the network; monitoring a latency associated with theprocessing of the data by the application; determining whether thelatency satisfies the latency criterion; responsive to the latencysatisfying the latency criterion, assigning resources to assist theapplication in reducing the latency associated with the processing ofthe data, the resources comprising computation resources, storageresources, network resources, or a combination thereof obtained from thecentral node, the regional node, the edge node, or any combinationthereof partitioning the API into a plurality of base APIs including afirst base API and a second base API, wherein the first base API hasassociated therewith a first latency less than a second latencyassociated with the second base API; and assigning the edge node to thefirst base API and the regional node to the second base API.
 11. Thedevice of claim 10, wherein the operations further comprise predictingthe latency based on the KPI.
 12. The device of claim 10, wherein theAPI comprises a plurality of base APIs, and wherein the databasecomprises a list of data objects to be accessed via the respective baseAPIs.
 13. The device of claim 12, wherein the operations furthercomprise triggering a procedure for reducing the latency comprisingplacement of the data objects at one or more of the edge node, theregional node, and the central node.
 14. The device of claim 12, whereinthe operations further comprise triggering a procedure for reducing thelatency comprising placement of the base APIs at one or more of the edgenode, the regional node, and the central node.
 15. A non-transitory,machine-readable medium comprising executable instructions that, whenexecuted by a processing system including a processor, facilitateperformance of operations comprising: receiving data at an edge node ofa plurality of edge nodes of a network, the network further comprising aplurality of regional nodes and a plurality of central nodes;determining a latency criterion associated with an application forprocessing the data, the application utilizing an applicationprogramming interface (API), information regarding the API being storedin a database accessible to at least one of the edge node, a regionalnode of the plurality of regional nodes in communication with the edgenode and a central node of the plurality of central nodes incommunication with the regional node, the API comprising a plurality ofbase APIs, the database comprising a list of data objects to be accessedvia the respective base APIs; monitoring a latency associated with theprocessing of the data by the application; determining whether thelatency satisfies the latency criterion; and responsive to the latencysatisfying the latency criterion, assigning resources to assist theapplication in reducing the latency associated with the processing ofthe data, the resources comprising computation resources, storageresources, network resources, or a combination thereof obtained from thecentral node, the regional node, the edge node, or any combinationthereof; and triggering a procedure for reducing the latency comprisingplacement of the data objects at one or more of the edge node, theregional node, and the central node.
 16. The non-transitory,machine-readable medium of claim 15, wherein the operations furthercomprise triggering a procedure for reducing the latency comprisingplacement of the base APIs at one or more of the edge node, the regionalnode, and the central node.
 17. The non-transitory, machine-readablemedium of claim 15, wherein the operations further comprise triggeringan iterative procedure for reducing the latency comprising placement ofthe data objects and the base APIs at one or more of the edge node, theregional node, and the central node.
 18. The non-transitory,machine-readable medium of claim 15, wherein the operations furthercomprise measuring a key performance indicator (KPI) of the network, andpredicting the latency based on the KPI.
 19. The non-transitory,machine-readable medium of claim 15, wherein the data is streamed fromthe edge node to the regional node for processing at the regional node.20. The non-transitory, machine-readable medium of claim 15, wherein thereceived data is accessible to a third party from the edge node, theregional node, the central node or a combination thereof.